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examples/vision/detection/paddledetection/quantize/python/README.md
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# PP-YOLOE-l量化模型 Python部署示例
本目录下提供的`infer.py`,可以帮助用户快速完成PP-YOLOE量化模型在CPU/GPU上的部署推理加速.
English | [简体中文](README_CN.md)
# PP-YOLOE-l Quantitative Model Python Deployment Example
`infer.py` in this directory can help you quickly complete the inference acceleration of PP-YOLOE quantization model deployment on CPU/GPU.
## 部署准备
### FastDeploy环境准备
- 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
- 2. FastDeploy Python whl包安装，参考[FastDeploy Python安装](../../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
## Deployment Preparations
### FastDeploy Environment Preparations
- 1. For the software and hardware requirements, please refer to [FastDeploy Environment Requirements](../../../../../../docs/en/build_and_install/download_prebuilt_libraries.md)
- 2. For the installation of FastDeploy Python whl package, please refer to [FastDeploy Python Installation](../../../../../../docs/en/build_and_install/download_prebuilt_libraries.md)
### 量化模型准备
- 1. 用户可以直接使用由FastDeploy提供的量化模型进行部署.
- 2. 用户可以使用FastDeploy提供的[一键模型自动化压缩工具](../../../../../../tools/common_tools/auto_compression/),自行进行模型量化, 并使用产出的量化模型进行部署.(注意: 推理量化后的分类模型仍然需要FP32模型文件夹下的infer_cfg.yml文件, 自行量化的模型文件夹内不包含此yaml文件, 用户从FP32模型文件夹下复制此yaml文件到量化后的模型文件夹内即可.)
### Quantized Model Preparations
- 1. You can directly use the quantized model provided by FastDeploy for deployment.
- 2. You can use [one-click automatical compression tool](../../../../../../tools/common_tools/auto_compression/) provided by FastDeploy to quantize model by yourself, and use the generated quantized model for deployment.(Note: The quantized classification model still needs the infer_cfg.yml file in the FP32 model folder. Self-quantized model folder does not contain this yaml file, you can copy it from the FP32 model folder to the quantized model folder.)
## 以量化后的PP-YOLOE-l模型为例, 进行部署
## Take the Quantized PP-YOLOE-l Model as an example for Deployment
```bash
#下载部署示例代码
# Download sample deployment code.
git clone https://github.com/PaddlePaddle/FastDeploy.git
cd /examples/vision/detection/paddledetection/quantize/python
#下载FastDeloy提供的ppyoloe_crn_l_300e_coco量化模型文件和测试图片
# Download the ppyoloe_crn_l_300e_coco quantized model and test images provided by FastDeloy.
wget https://bj.bcebos.com/paddlehub/fastdeploy/ppyoloe_crn_l_300e_coco_qat.tar
tar -xvf ppyoloe_crn_l_300e_coco_qat.tar
wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
# 在CPU上使用ONNX Runtime推理量化模型
# Use ONNX Runtime inference quantization model on CPU.
python infer_ppyoloe.py --model ppyoloe_crn_l_300e_coco_qat --image 000000014439.jpg --device cpu --backend ort
# 在GPU上使用TensorRT推理量化模型
# Use TensorRT inference quantization model on GPU.
python infer_ppyoloe.py --model ppyoloe_crn_l_300e_coco_qat --image 000000014439.jpg --device gpu --backend trt
# 在GPU上使用Paddle-TensorRT推理量化模型
# Use Paddle-TensorRT inference quantization model on GPU.
python infer_ppyoloe.py --model ppyoloe_crn_l_300e_coco_qat --image 000000014439.jpg --device gpu --backend pptrt
```

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[English](README.md) | 简体中文
# PP-YOLOE-l量化模型 Python部署示例
本目录下提供的`infer.py`,可以帮助用户快速完成PP-YOLOE量化模型在CPU/GPU上的部署推理加速.
## 部署准备
### FastDeploy环境准备
- 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
- 2. FastDeploy Python whl包安装，参考[FastDeploy Python安装](../../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
### 量化模型准备
- 1. 用户可以直接使用由FastDeploy提供的量化模型进行部署.
- 2. 用户可以使用FastDeploy提供的[一键模型自动化压缩工具](../../../../../../tools/common_tools/auto_compression/),自行进行模型量化, 并使用产出的量化模型进行部署.(注意: 推理量化后的分类模型仍然需要FP32模型文件夹下的infer_cfg.yml文件, 自行量化的模型文件夹内不包含此yaml文件, 用户从FP32模型文件夹下复制此yaml文件到量化后的模型文件夹内即可.)
## 以量化后的PP-YOLOE-l模型为例, 进行部署
```bash
#下载部署示例代码
git clone https://github.com/PaddlePaddle/FastDeploy.git
cd /examples/vision/detection/paddledetection/quantize/python
#下载FastDeloy提供的ppyoloe_crn_l_300e_coco量化模型文件和测试图片
wget https://bj.bcebos.com/paddlehub/fastdeploy/ppyoloe_crn_l_300e_coco_qat.tar
tar -xvf ppyoloe_crn_l_300e_coco_qat.tar
wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
# 在CPU上使用ONNX Runtime推理量化模型
python infer_ppyoloe.py --model ppyoloe_crn_l_300e_coco_qat --image 000000014439.jpg --device cpu --backend ort
# 在GPU上使用TensorRT推理量化模型
python infer_ppyoloe.py --model ppyoloe_crn_l_300e_coco_qat --image 000000014439.jpg --device gpu --backend trt
# 在GPU上使用Paddle-TensorRT推理量化模型
python infer_ppyoloe.py --model ppyoloe_crn_l_300e_coco_qat --image 000000014439.jpg --device gpu --backend pptrt
```